- Видео 114
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Data & Science with Glen Wright Colopy
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Добавлен 15 ноя 2019
Data and Science with Glen Wright Colopy is a podcast covering critical scientific reasoning, particularly from a data science / machine learning / statistics perspective. Episodes typically focus on understanding of how to be better scientists and critical thinkers for the practical purpose of being a better data scientists.
Glen Wright Colopy (host) has a doctorate from Oxford University (DPhil) in Bayesian nonparametric modeling for time-series analysis, optimization, and anomaly/novelty detection in patient monitoring. Hence, we tend to cover healthcare and life science applications a bit more often. (But I'm getting better with covering a wider variety of scientific domains and applications!)
Previously called "Pod of Asclepius".
Website: www.podofasclepius.com
Podbean: podofasclepius.podbean.com
iTunes: podcasts.apple.com/us/podcast/the-pod-of-asclepius/id1494970096?mt=2&app=podcast
Stitcher www.stitcher.com/podcast/the-pod-of-asclepius
Glen Wright Colopy (host) has a doctorate from Oxford University (DPhil) in Bayesian nonparametric modeling for time-series analysis, optimization, and anomaly/novelty detection in patient monitoring. Hence, we tend to cover healthcare and life science applications a bit more often. (But I'm getting better with covering a wider variety of scientific domains and applications!)
Previously called "Pod of Asclepius".
Website: www.podofasclepius.com
Podbean: podofasclepius.podbean.com
iTunes: podcasts.apple.com/us/podcast/the-pod-of-asclepius/id1494970096?mt=2&app=podcast
Stitcher www.stitcher.com/podcast/the-pod-of-asclepius
Keith O'Rourke | The Logic of Statistics
Keith O'Rourke | The Logic of Statistics
#statistics #datascience #science
Dr. Keith O'Rourke talks about the logical reasoning behind statistical modeling. Topics include mathematical vs scientific reasoning, whether science has become too stats focused, and vice versa.
Watch it on...
RUclips: ruclips.net/video/FqE4ROHBKpY/видео.html
Podbean: dataandsciencepodcast.podbean.com/e/keith-o-rourke-the-logic-of-statistics/
0:00 - The logic of statistics
0:30 - What is scientific statistics?
5:15 - The logic of statistics and CS Pierce
9:15 - Role of representation in statistics: explicit vs implicit
14:13 - Diagrammatic Reasoning
18:45 - Why is modeling counterfactual?
19:33 - How can statisticians becom...
#statistics #datascience #science
Dr. Keith O'Rourke talks about the logical reasoning behind statistical modeling. Topics include mathematical vs scientific reasoning, whether science has become too stats focused, and vice versa.
Watch it on...
RUclips: ruclips.net/video/FqE4ROHBKpY/видео.html
Podbean: dataandsciencepodcast.podbean.com/e/keith-o-rourke-the-logic-of-statistics/
0:00 - The logic of statistics
0:30 - What is scientific statistics?
5:15 - The logic of statistics and CS Pierce
9:15 - Role of representation in statistics: explicit vs implicit
14:13 - Diagrammatic Reasoning
18:45 - Why is modeling counterfactual?
19:33 - How can statisticians becom...
Просмотров: 1 473
Видео
Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks
Просмотров 4122 года назад
Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks Did you know that it's possible to hide malware in neural networks? Actually, you can hide malware in many statistical models. This is the subject of two recently-published papers (aptly titled "EvilModel" & "EvilModel 2.0"). Dr. Jack Fitzsimons makes it easy to understand how this is done, using techniques that began long before ...
Scott Cunningham | Causal Inference (The Mixtape)
Просмотров 2,9 тыс.2 года назад
Scott Cunningham | Causal Inference (The Mixtape) Scott Cunningham (Baylor University) discusses the ideas of his book "Causal Inference: The Mixtape". Topics include trusting inference in the absence of counterfactuals and the challenges of apply scientific methods to social phenomena. Watch it on... RUclips: ruclips.net/video/yNaCudDVTkY/видео.html Podbean: dataandsciencepodcast.podbean.com/e...
Eric Daza | Important Ideas in Causal Inference
Просмотров 8252 года назад
Eric Daza | Important Ideas in Causal Inference Andrew Gelman and Aki Vehtari wrote a paper titled, "What are the most important statistical ideas of the past 50 years?". The first idea in the list is "counterfactual causal inference". Eric Daza (Evidation Health) walks us through the main ideas of the Gelman & Vehtari paper, drawing examples from several fields, including medical & healthcare ...
Ruda Zhang | Gaussian Process Subspace Regression
Просмотров 4962 года назад
#datascience #statistics #ai Ruda Zhang | Gaussian Process Subspace Regression Ruda Zhang (Duke University) walks us through "Gaussian Process Subspace Regression for Model Reduction" by Zhang, Mak, and Dunson. To keep the topic interesting for both the early career & advanced audience we recap key points at a high level so that no one gets lost. This episode involves a presentation, so you may...
Math-Science Duality | Ruda Zhang
Просмотров 3972 года назад
Ruda Zhang | Math-Science Duality Watch it on... RUclips: ruclips.net/video/GoDwen-RGZg/видео.html Podbean: dataandsciencepodcast.podbean.com/e/ruda-zhang-math-science-duality/ Statistics is thought to reside at the interface of science and mathematics. Ruda Zhang (Duke University) discusses the friction at this interface and the role that both mathematical formalism & observational/data-driven...
Integrating Science into Stats Models | Simon Mak
Просмотров 1,1 тыс.2 года назад
Simon Mak | Integrating Science into Stats Models #statistics #science #ai It’s a common dictum that statisticians need to incorporate domain knowledge into their modeling and the interpretation of their results. But how deeply can scientific principles be embedded into statistical models? Prof. Simon Mak (Duke University) is pushing this idea to the limit by integrating fundamental physics, ph...
Martin Goodson | Practical Data Science & The UK's AI Roadmap
Просмотров 2382 года назад
#ai #datascience #startups Martin Goodson | Practical Data Science & The UK's AI Roadmap Martin Goodson (Evolution AI) describes the key aspects of the UK's AI Roadmap & responses to the document by members of the Royal Statistical Society. In particular, Martin describes the disconnect between the priorities of AI startups and industry practitioners on one side, and government and academia on ...
Jack Fitzsimons | Data Security, Privacy, & Artificial Intelligence
Просмотров 3142 года назад
Dr. Jack Fitzsimons (Oblivious AI) gives a high-level introduction to the technologies that can either exploit or protect your data privacy. If you'd like to survey the landscape of data privacy-preserving technologies (from someone who's building the tech) this is a good place to start! #datascience #privacy #ai 0:00 - Coming up... 3:24 - Introduction 6:20 - Data privacy and privacy enhancing ...
Chris Tosh | The piranha problem in statistics
Просмотров 6122 года назад
Chris Tosh | The piranha problem in statistics The piranha problem (too many large, independent effect sizes influence the same outcome) has received some attention on Andrew Gelman’s blog. But now it’s a paper! Chris Tosh (Memorial Sloan Kettering) talks about multiple views of the piranha problem and detecting the implausible scientific claims that are published. The butterfly effect makes an...
Chris Holmes | AI, Digital Health, & The Alan Turing Institute
Просмотров 5472 года назад
Chris Holmes is Professor of Biostatistics at the University of Oxford and Programme Director for Health and Medical Sciences at The Alan Turing Institute. Chris’ research interests include Bayesian nonparametrics (which is the right kind of nonparametrics), statistical machine learning, genomics, and genetic epidemiology. 0:00 - Intro 1:38 - Chris Holmes, Professor of Biostatistics at Oxford U...
Charlotte Deane | Bioinformatics, Deepmind's AlphaFold 2, and Llamas
Просмотров 1,2 тыс.2 года назад
Charlotte Deane | Bioinformatics, Deepmind's AlphaFold 2, and Llamas #datascience #ai Charlotte Deane (Oxford University) talks about statistical approaches to bioinformatics, the evolution of Google Deepmind's AlphaFold 2 & its place in protein informatics deep learning landscape. She also describes humanizing antibodies, and the increasing role of software engineers in statistical research gr...
Eric Schwitzgebel | Consciousness, Zombies, & First Person Data | Philosophy of Data Science
Просмотров 5263 года назад
The philosophical community continuously aims to reconcile differing views on first person data and the consciousness of the mind. Is it possible to live without consciousness? Can one conceive thoughts without matching images to them? In this episode, Eric Schwitzgebel of the University of California tries to dissect such topics and questions to help us better understand the philosophical worl...
Starting a Statistics Consultancy | Janet Wittes
Просмотров 6993 года назад
Starting a Statistics Consultancy | Janet Wittes The following interview was a keynote fireside chat with Janet Wittes (Statistics Collaborative, Inc.) titled "Statisticians as Entrepreneurs". It was recorded for the BBSW 2021 Conference (Nov 3 - 5 in Foster City, CA). We thought that others might enjoy learning from Janet's experience building a statistics consultancy company. Enjoy! Reference...
Jingyi Jessica Li | Advancing Statistical Genomics | Philosophy of Data Science
Просмотров 7843 года назад
Jingyi Jessica Li | Advancing Statistical Genomics | Philosophy of Data Science
Mine Çetinkaya-Rundel | Advancing Open Access Data Science Education
Просмотров 3303 года назад
Mine Çetinkaya-Rundel | Advancing Open Access Data Science Education
Jingyi Jessica Li | Statistical Hypothesis Testing versus Machine Learning Binary Classification
Просмотров 9483 года назад
Jingyi Jessica Li | Statistical Hypothesis Testing versus Machine Learning Binary Classification
Gualtiero Piccinini | An Introduction to First-Person Data
Просмотров 4033 года назад
Gualtiero Piccinini | An Introduction to First-Person Data
David Dunson | Advancing Statistical Science | Philosophy of Data Science
Просмотров 1,5 тыс.3 года назад
David Dunson | Advancing Statistical Science | Philosophy of Data Science
Martin Kuldorff | Spatiotemporal Models of Disease Outbreaks
Просмотров 4603 года назад
Martin Kuldorff | Spatiotemporal Models of Disease Outbreaks
Iterating the value of data science in industry| Jason Costello
Просмотров 823 года назад
Iterating the value of data science in industry| Jason Costello
Putting Machine Learning Models into Software| Jason Costello
Просмотров 1043 года назад
Putting Machine Learning Models into Software| Jason Costello
First step to delivering value to industry | Jason Costello
Просмотров 1003 года назад
First step to delivering value to industry | Jason Costello
Simple visualizations can lead to cool data science projects| Jason Costello
Просмотров 1133 года назад
Simple visualizations can lead to cool data science projects| Jason Costello
Data Science Projects Can Fail. | Jason Costello
Просмотров 833 года назад
Data Science Projects Can Fail. | Jason Costello
Software and Deduction, Machine Learning and Induction | Jason Costello
Просмотров 1063 года назад
Software and Deduction, Machine Learning and Induction | Jason Costello
Monitoring Patients & Turbine Engines | Jason Costello
Просмотров 663 года назад
Monitoring Patients & Turbine Engines | Jason Costello
Transitioning from Academic to Industrial Data Science| Jason Costello
Просмотров 2373 года назад
Transitioning from Academic to Industrial Data Science| Jason Costello
Jason Costello | Data Science vs Software, Academia vs Industry
Просмотров 4353 года назад
Jason Costello | Data Science vs Software, Academia vs Industry
Mark Hallen | Exploding Algorithms, Scientific Priorities, and Molecular Discovery
Просмотров 2443 года назад
Mark Hallen | Exploding Algorithms, Scientific Priorities, and Molecular Discovery
can you tell me how to use Wound Healing process Modeling using partial Differential equation
Taking copious notes.
❤❤❤
My great honor to be taught by Professor Evans.
As a statistics student, I find talks like these especially enjoyable..so thanks!
👍
I sent you an email Martin Goodson.. My name is Martin Onassis Goodson.. I sent you an email about 4years ago.. I got interested in technology by looking at your RUclips videos..
Unless one has read (and re-read) very carefullly both of Mayo's most recent books (and I have) it is not possible to authentically appreciate her genius, breadth of knowledge, and careful clarity of thought. She is a very lucid thinker and writer. This interview on RUclips is a surprising disappointment for me but not because of anything that she is saying. The sharp disappointments I have are with the host. I do not know his credentials but from the rambling structure and content of his overly long questions, I perceive that he is in way over his head. He is visibly ill at ease. It is highly distracting for me that he repeatedly says, "like" and uses it as a verbal crutch. I actually lost count of the number of "likes" I heard. As well, he spews the odious filler "you know" over and over in his remarks -- this sort of verbiage choice makes him sound exactly like the teenaged kids seen hanging around with their well-used skateboards near shopping malls all over 'Murika.
Very insightful!
pHARMa scamdemia poisoned 2/3rds of the planet
I enjoy this podcast! That’s a well knowledgeable young man ❤
Keith passed away in late November. He was a mentor and friend to me; until the pandemic intervened we would have lunch together every few months and discuss statistics, machine learning, philosophy, and so forth. This video is basically my last chance to spend some time with him and gain the benefit of his wisdom.
Next time add the link to your code
Thanks alot for sharing this,,,share more videos
Deborah, there is nothing problematic about Popper’s demarcation. I consistently ask people what it’s problems are and you realise they are just straightforward misunderstandings. Your elaboration is no exception. Popper took all what you say it account. He said that the rule for ‘face saving’ adjustments should be that the auxiliary hypothesis that adjust them should be independently testable. It does not follow from Popper’s demarcation that if something is falsified then it is science. His demarcation is a necessary property not a sufficient one. It only claim that if something is not falsifiable then it is not scientific. How can you be a philosopher of science with and award and make this elementary year 1 undergraduate mistake? It is not Popper’s fault that there isn’t a demarcation of science that will make it anymore warranted than any other investigation. Why is it that only Popper had put a demarcation forward that doesn’t succumb to this obviously erroneous way of delineating science. Yet his method, though not justified is effective. What else do you want: the unicorn of warranted assertsbility…😢 this is not the dark ages, where people’s assertions have to be vetted by some self-appointed clergy. Why are scientists trying to usurp that role and why are philosophers trying to help them? All you need is a republic of science where each participant is open to criticism, and just open to criticism but actively seeking it. Continue to promote a philosophy of science that thinks that we have to find some way do justify our claims and also that justifying makes them what - less suceptical to being wrong? - then you will constantly stifle the only idea that solves any of these problems in science - the idea that theories should be constantly and ceaselessly critically investigated and no amount of evidence justified them, and you will constantly be a participant in encouraging the very thing you want to disappear: the attempt to pretend that your theories are exalted by evidence. Your view leaves a back door to that kind of misuse. Self-sabotaging. Any theory of science that says there is some way to make scientific theories compelling will always have methods that can be abused to make theories some more compelling than they are. Theories can never be justified not one bit. Pretending that they can is the whole problem.
Hi, thanks for the video!
great podcast
I enjoyed this episode can you invite other statistical consultants in your future vids?
We're going to do more than that. Stay tuned....
thank you for the video .... can you give us some references about change point detection with a statistical approach
I really agree with what you said at the 18-20 minute mark. There is a big issue I am facing as someone trained in stats in regards to domain knowledge. In stats, DS, CS we learn all these tools but ultimately don't learn enough domain knowledge. And the way things are headed domain-specific modeling is becoming the "next big thing" (eg DAGs, incorporating inductive biases, priors, neural architectures, etc). Without the science aspect I fear statisticians are going to be left behind. The math/stats/programming for an applied setting is the easier part in my opinion and you now even see so many domain experts in chemE, BME, bioinfo, chem, physics etc picking up DS/ML skills. They may not be as rigorous as a statistician but the domain expertise is more important than "is my estimate perfectly unbiased and is my inference type 1 error calibrated" for modern modeling
Thou shalt not analyze data until *everyone* is satisfied with the asymptotic guarantees of your minimum-variance unbiased estimator! That's the rule.
Thanks for the comment, btw. the "type 1 error" bit reminded me of a topic that I wanted to cover about redundancy in multiplicity corrections.
Thnk u for this series and greetings from peru, as a undegrad in math and statistics u touch topics that matter for applied math and the role of data in society
Thanks, Gonzalo! In the future I'd like to have some conversations contrasting the way that statisticians vs applied mathematicians approach scientific modeling & discovery.
C.S. Peirce is pronounced “Purse”
Sorry - posted this before I heard Dr. O’Rourke pronounce it correctly ;)
👍
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thank u for keeping this series !
Thanks! We'll have another causal inference episode next week.
Soon there will be an Environmental Information Technology standard . Much like Health information technology and traditional IT
Loving the unexpected dry nerdy humor. Great show. Very interesting angles.
Ceviri yaparak bu faydali bilgileri dinledim lutfen alt yaziyla videolarin devamini bekliyorum
Bu faydalı bilgiler için teşekkürler
verdiğiniz bilgiler için teşekkür ederiz videoların devamını bekleriz
bu günde bilgi kumbaramıza birkaç kuruş attık sayenizde teşşekkürler<3
çok güzel ve bilgilendirici bir video olmuş gerçekten emeğinize sağlık
Evren hakkında daha neler öğreneceğiz sayenizde
Altyazı eklenirse sizin için etkileşim bakımından daha iyi olur👌
Emeğinize sağlık çok güzel anlatımlı bir video olmuş 😊👏
Evren gizemlerle dolu bunlardan bazılarınızı açıklamanız çok güzel
Altyazı eklenirse daha büyük kitleler faydalanabilir diye düşünüyorum
Bence fizik sevenler buraya harika bir video olmus
Bilgilendirici
Başarılarınızın devamını diliyorum güzel bir anlatım 👍
gercekten mukemmel bır konusma olmus emegınıze saglık
Faydalı heralde ugraslas icin teşekkürler İngilizce bilmkek gerek
Gercekten mukemmel bır konusma olmus
Gercekten mukemmel bır konusma olmus emegınıze saglık
I think it is a helpful video
Evren gizemlerle dolu bunlardan bazılarınızı açıklamanız çok güzel
Temel fizik ve biyolojiyi model çıkarımına entegre ederek anlatmışsınız çok güzel bir çalışma olmuş. 👏👏👏
Emeğinize sağlık çok güzel ve kaliteli bir video olmuş💯🙏🏻
Harika olmuş devamını bekleriz
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